CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning

Extreme Multi-label Learning (XML) considers large sets of items described by a number of labels that can exceed one million. Tree-based methods, which hierarchically partition the problem into small scale sub-problems, are particularly promising in this context to reduce the learning/prediction complexity and to open the way to parallelization... (read more)

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